Mante, J;
Gangadharan, N;
Sewell, DJ;
Turner, R;
Field, R;
Oliver, SG;
Slater, N;
(2019)
A heuristic approach to handling missing data in biologics manufacturing databases.
Bioprocess and Biosystems Engineering
, 42
(4)
pp. 657-663.
10.1007/s00449-018-02059-5.
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Abstract
The biologics sector has amassed a wealth of data in the past three decades, in line with the bioprocess development and manufacturing guidelines, and analysis of these data with precision is expected to reveal behavioural patterns in cell populations that can be used for making predictions on how future culture processes might behave. The historical bioprocessing data likely comprise experiments conducted using different cell lines, to produce different products and may be years apart; the situation causing inter-batch variability and missing data points to human- and instrument-associated technical oversights. These unavoidable complications necessitate the introduction of a pre-processing step prior to data mining. This study investigated the efficiency of mean imputation and multivariate regression for filling in the missing information in historical bio-manufacturing datasets, and evaluated their performance by symbolic regression models and Bayesian non-parametric models in subsequent data processing. Mean substitution was shown to be a simple and efficient imputation method for relatively smooth, non-dynamical datasets, and regression imputation was effective whilst maintaining the existing standard deviation and shape of the distribution in dynamical datasets with less than 30% missing data. The nature of the missing information, whether Missing Completely At Random, Missing At Random or Missing Not At Random, emerged as the key feature for selecting the imputation method.
| Type: | Article |
|---|---|
| Title: | A heuristic approach to handling missing data in biologics manufacturing databases |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s00449-018-02059-5 |
| Publisher version: | http://dx.doi.org/10.1007/s00449-018-02059-5 |
| Language: | English |
| Additional information: | OpenAccess This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Biologics manufacturing data, Missing data, Imputation, Parameter recurrence, Data pre-processing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10109706 |
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